2. Model Theory¶

2.1. Introduction to land surface process modelling, week 1¶

2.1.1. Key topics¶

General introduction to land surface process modelling.
Forward modelling
Aims of modelling
Model development cycle

2.1.2. Literature for exam¶

Wainwright, J. and Mulligan, M., 2004, Modelling and model building, in: Environmental Modelling: finding simplicity in complexity, Second Edition. J. Wainwright, M. Mulligan (eds), p. 7-26, Wiley, Chichester.

Karssenberg, D., 2010, Introduction to dynamic spatial environmental modelling.

Burrough, P.A., McDonnel, R. & Lloyd, C.D., 2015, Principles of Geographical Information Systems, Oxford University press, Chapter 12, Space-time modelling and error propagation, p. 251-260.

2.1.3. Reading material¶

Karssenberg, D., Bridge, J.S., 2008, A three-dimensional numerical model of sediment transport, erosion and deposition within a network of channel belts, floodplain and hill slope: extrinsic and intrinsic controls on floodplain dynamics and alluvial architecture, Sedimentology, 55, 1717-1745. Link.

2.1.4. Lectures, e-Lectures¶

Lecture slides Introduction to the course

e-Lecture Introduction to simulation modelling

Lecture slides Introduction to simulation modelling

2.2. Local models, week 2¶

2.2.1. Key topics¶

Dynamic point models
Numerical solution of differential equations

2.2.2. Literature for exam¶

Kreyszig, E., 1999, Numerical Methods for Differential Equations, in Advanced Engineering Mathematics, New York, N.Y., Wiley: p. 942-952.

2.2.3. Lectures, e-Lectures¶

e-Lecture Point models and differential equations - 01

e-Lecture Point models and differential equations - 02

e-Lecture Point models and differential equations - 03

Lecture slides Point models and differential equations - 01

Lecture slides Point models and differential equations - 02

Lecture slides Point models and differential equations - 03

2.3. Spatial models, week 3¶

2.3.1. Key topics¶

Neighbourhood interaction
Neighbourhoods by a defined topology
Dynamic neighbourhood models: cellular automata

2.3.2. Literature for exam¶

Burrough, P.A., McDonnel, R. & Lloyd, C.D., 2015, Principles of Geographical Information Systems, Oxford University press, Chapter 7, Analysis of discrete entities in space, p. 127-145, and Chapter 10, Analysis of continuous fields, p. 201-229.

Favis-Mortlock, D., 2004, Non-linear dynamics, self-organization and cellular automata models, in: Environmental Modelling: finding simplicity in complexity, J. Wainwright, M. Mulligan (eds), p. 45-67, Wiley, Chichester.

2.3.3. Reading material¶

Saco, P.M., Willgoose, G.R., Hancock, G.R., 2007, Eco-geomorphology of banded vegetation patterns in arid and semi-arid regions, Hydrology and Earth System Sciences, 11: 1717-1730. Link.

2.3.4. Lectures, e-Lectures¶

e-Lecture Neighbourhood interaction

e-Lecture slides Spatio-temporal models: neighbourhood interaction, pdf

2.3.5. Working group session¶

We will have a working group session on this topic.

To prepare for the session:

Listen to the e-Lecture (see above for the link)
Study the literature for the exam (related to this topic, see above)
Create a group in Blackboard, available at Course Content -> Working Groups
Prepare an 8 minute presentation (one per group), for topics see below
At the end of the presentation include one slide with discussion topics to steer the discussion after your talk

During the working group session:

Each group gives an 8 minute presentation
After each presentation: 4 minutes discussion with questions

The presentation should describe an example of either 1) the use of cellular automata or 2) self organisation in the earth sciences (or related fields). Search the literature (use a bibliographic database, e.g. http://www.scopus.com) to find at least one paper on one these topics (examples of applications of cellular automata are land use change, forest fire, vegetation growth and dispersal, disease spreading, lava flows; for self organisation there are also many examples). Prepare a presentation which explains how cellular automata are used in the article or what kind of self organisation is described. If you want you can add items for discussion at the end.

2.3.6. Short paper assignment¶

The short paper assignment is done in a group of students. Please self-subscribe to a group in Blackboard (Course Content -> Short Paper Groups).

Favis-Mortlock (2004, in the reader) discusses self-organizing systems and why feedback mechanisms may lead to self-organization. Read the paper by Saco et al. (reading material). In a short paper (max. 1000 words excluding the bibliography, not longer), explain the concept of self-organization and discuss why the system studied by Saco et al. is a self-organizing system. In addition, provide the main feedback mechanisms that lead to the observed self-organization. Hand in by uploading in Blackboard.

2.4. Stochastic models, week 4¶

2.4.1. Key topics¶

Stochastic variables
Probability distributions, categorial and continuous variables
Properties of probability distributions: percentiles, confidence intervals
Stochastic variables to represent uncertain model inputs and parameters
Solving stochastic models: Monte Carlo simulation

2.4.2. Literature for exam¶

Karssenberg, D. de Jong, K., 2005, Dynamic environmental modelling in GIS: 2. Modelling error propagation. International Journal of Geographical Information Science, 19, p. 623-637.

Karssenberg, D., Schmitz, O., Salamon, P., De Jong, K. and Bierkens, M.F.P., 2010, A software framework for construction of process-based stochastic spatio-temporal models and data assimilation. Environmental Modelling & Software, 25, pp. 489-493.

Kreyszig, E., 1999, Data Analysis. Probability Theory, in Advanced Engineering Mathematics, New York, N.Y., Wiley, Chapter 22, the following pages:

Pages 1050-1064, except 22.4 (Permutations and Combinations), Problem Sets and Examples

2.4.3. Reading material¶

No reading material.

2.4.4. e-Lectures¶

e-Lecture Introduction to Stochastic Modelling

e-Lecture Monte Carlo simulation

e-Lecture slides Stochastic models, Monte Carlo simulation, pdf

2.5. Agent-based models, week 5¶

2.5.1. Key topics¶

Agents vs Fields
Agent representations
Examples

2.5.2. Literature for exam¶

Macal, C.M., North, M.J., 2010, Tutorial on agent-based modelling and simulation. Journal of Simulation, 4, pp. 151-162.

Railsback, S.F., 2001, Concepts from complex adaptive systems as a framework for individual-based modelling. Ecological modelling, 139, pp. 47-62. Link.

2.5.3. Reading material¶

Bennett, D.A., Tang, W., 2006, Modelling adaptive, spatially aware, and mobile agents: Elk migration in Yellowstone. International Journal of Geographical Information Science, 20, pp. 1039-1066. Link.

2.5.4. e-Lectures¶

e-Lecture Spatial agent-based modelling

Lecture slides Introduction to spatial agent-based modelling, pdf

2.6. Calibration, week 6¶

2.6.1. Key topics¶

Objective function
Minimizing the objective function: hillclimbing, brute force and other techniques

2.6.2. Literature for exam¶

Beven, K.J., 2002, Parameter estimation and predictive uncertainty, in Rainfall-runoff modelling, the primer, Wiley, Chichester, p. 217-233.

Janssen, P.H.M, Heuberger, P.S.C., 1995, Calibration of process-oriented models, Ecological Modelling 83, 55-66.

2.6.3. Reading material¶